Large Format Topographic Map Segmentation Method Based on Random Probability Sampling and Multilevel Fusion

Abstract

The invention discloses a large-area?topographic map?segmentation method based on random probability?sampling and multi-level fusion. The method comprises steps: 1) an original?topographic map image is imputed; 2) random probability?sampling is carried out on the original?topographic map image; 3) the number of topographic map?segmentation types is determined; 4) a clustering center is calculated and a clustering center matrix is obtained; 5) a membership matrix of the original?topographic map is calculated and a membership matrix is obtained; 6) the membership matrix is used for carrying out fuzzy classification on the original?topographic map and segmented split images are obtained; 7) multi-level fusion is carried out on the segmented images and a fused image is obtained; and 8) the segmented split images are outputted. According to the method, random probability?sampling and multi-level image fusion are combined to carry out topographic map?segmentation, segmented split images of the topographic map can finally be more accurately acquired, the segmentation efficiency is greatly improved, and the method is particularly suitable for large-area?topographic map?segmentation.

Description

technical field [0001] The invention belongs to the technical field of geographic information processing, and in particular relates to a large-format topographic map segmentation method based on random probability sampling and multi-level fusion. The invention is applied to image segmentation of large-format topographic maps. Background technique [0002] In the field of topographic map processing, in order to quickly and accurately segment topographic maps to obtain segmented maps and provide favorable conditions for further extraction of geographical elements in topographic maps, it is usually necessary to segment topographic maps. Currently, topographic map segmentation methods mainly use clustering based on color space and classification based on sample learning. [0003] Zheng Huali and others proposed in the document "Zheng Huali, Zhou Xianzhong, Wang Jianyu, 'Automatic color separation of topographic maps based on color space conversion and fuzzy constraint clusterin...

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Problems solved by technology

[0006] Aiming at the shortcomings of unsatisfactory and inefficient topographic map segmentation caused by color space-based clustering and sample learning-based classification methods in the above-mentioned prior art, the purpose of the present invention is to propose a method based on random probability sampling and multi-level fusion The large-scale topographic map segmentation method, the segmentation of the topographic map in this method mainly includes three parts: obtaining the cluster center, fuzzy classification and multi-level fusion

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